Monthly Archives: February 2013

Algorithmic trading is all the rage in India right now, and across the market the view is unanimous: the only way is up. The question is how just fast it will grow.

By the start of 2012, Algo accounted for some 24 percent of cash equities turnover in India and about 30 percent of equity derivatives. According to figures from the Bombay Stock Exchange, by far the smaller of the two Indian exchanges that dominate equities trading, the share for equity derivatives has already jumped to 45 percent since then.

Algorithms and High frequency trading are the hottest topics in the market – algorithmic trading and HFT itself, and now the regulations around it. This is what the majority of players in the market are focused on today.

India has the building blocks in place for a ramp-up. Co-location has been available from both the Bombay Stock Exchange and its bigger competitor, the National Stock Exchange, for 18 months. Both exchanges, and market observers, say their trading platforms can handle HFT. Direct market access is available. Smart order routing between the two exchanges has also been operating since August 2010.

The Indian regulator, the Securities and Exchange Board of India (SEBI), produced guidelines for algorithmic trading in March which brokers, exchanges and market watchers hail as a sensible response. The new rules, they say, recognise that algorithmic trading is a natural development and are aimed at preventing problems but not blocking growth.

“All the dynamics point to an increase in automated and algo trading in the next few years,” Expect the cash equities and derivatives levels to raise around 50-55 percent within the next year or so.

Algorithms – step-by-step mathematical procedures – generate automatic trades, conducted by computers, each one racing to be first. And while some computers do receive news about the outside world in electronic format, many high-frequency trading algorithms are simply responding to the hectic world of the electronic trading floor.

Humans still watch the systems, but the computers move far too quickly for us to react to everything they do. To give you a sense of how fast high-frequency trading can be, in the time it takes Usain Bolt to react to the starting pistol, a high-frequency trading platform could complete about 165,000 separate trades.

Now this isn’t quite as insane as it sounds. These computers, all competing with each other, are a lot cheaper and more efficient than human traders trying to match bids to buy and offers to sell. So within reason, automated, high-frequency trading is a good thing. But it’s possible to have too much of a good thing.

High-Frequency Trading are divided into five categories:

First, there are algorithms designed not to lose money while executing a trade that’s been placed by a human. If you try to buy a large block of shares all at once, for instance, you might find that there aren’t enough potential sellers and you’ll have to wait for others to show up.

Other computers may see that you’ve got this large unfilled order and exploit it, perhaps by snapping up shares and selling to you at a profit. To avoid this problem you can ask a computer to slice up your big trade into smaller, more subtle pieces.

Then there are algorithms designed simply to make money by finding buyers and sellers with a little margin between them.

Third, there are algorithms which find statistical relationships between different shares or bonds, and when the statistical relationship fails to hold – even for a moment – they jump in and make a bet that normal service will be resumed. These are called statistical arbitrage algorithms.

So far, so good – it would be hard to find many people in finance who would consider these three types of high-frequency trading to be immoral.

But there are two rather more predatory strategies. One is called algo-sniffing. Here, a super-fast computer tries to find other computers going about their everyday business of buying or selling shares, and figures out what they’re going to do and when.
The algo-sniffer can then get ahead of the game and exploit the slower computer. And of course you could have algo-sniffer-sniffers and algo-sniffer-sniffer-sniffers in a high-frequency arms race. No wonder speed can be so important.

And finally, a particular sub-category of the algo-sniffer is the spoofer, which deliberately makes fake offers designed to lure other computers to show their hands, then cancels the offers. Spoofing might be illegal, or at least against the rules of stock exchanges, but it’s hard to prove that it’s going on.